Subha Maity

According to our database1, Subha Maity authored at least 13 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Estimating Fréchet bounds for validating programmatic weak supervision.
CoRR, 2023

An Investigation of Representation and Allocation Harms in Contrastive Learning.
CoRR, 2023

Simple Disentanglement of Style and Content in Visual Representations.
Proceedings of the International Conference on Machine Learning, 2023

Understanding new tasks through the lens of training data via exponential tilting.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Bayes classifier cannot be learned from noisy responses with unknown noise rates.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022
Minimax optimal approaches to the label shift problem in non-parametric settings.
J. Mach. Learn. Res., 2022

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions.
J. Mach. Learn. Res., 2022

How does overparametrization affect performance on minority groups?
CoRR, 2022

RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups.
Proceedings of the 2022 IEEE Visualization and Visual Analytics (VIS), 2022

2021
Does enforcing fairness mitigate biases caused by subpopulation shift?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Statistical inference for individual fairness.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
There is no trade-off: enforcing fairness can improve accuracy.
CoRR, 2020


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